Forecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange)
Subject Areas : International Journal of Finance, Accounting and Economics StudiesAli Anvary Rostamy 1 , Nor Mousazadeh Abbasi 2 , Mohammad Ali Aghaei 3 , Mahdi Moradzadeh Fard 4
1 - Professor, Accounting and Finance Department, Faculty of Management and Economics, Tarbiat Modares University (TMU).
2 - Master in Accounting, Faculty of Management and Economics, Tarbiat Modares University (TMU).
3 - Assistant Professor, Accounting and Finance Department, Faculty of Management and Economics, Tarbiat Modares University
4 - Assistant Professor, Accounting and Finance Department, Islamic Azad University, Karaj Branch.
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* Abraham,A., & B. Nath, P.K. Mahanti.(2001). Hybrid intelligent systems for stock market analysis, in: V.N. Alexandrov, J. Dongarra, B.A. Julianno, R.S. Renner, C.J.K. Tan (Eds.), Computational Science, Springer-Verlag, Germany, pp. 337–345.
* Abramovich,F. & Besbeas,P.& Sapatinas,T.(2002). Empirical Bayes approach to block wavelet function estimation. Computational Statistics and Data Analysis , 435–451.
* Adam, A,M. & Twenboah, G. (2008). Macroeconomic factors and stock market movement: Evidence Ghana, Monich Personal RePEc Archive.
* Aiken, M., & Bsat, M. (1999). Forecasting market trends with neural networks. Information Systems Management, 16(4), 42–48.
* Anderson J.A. ( 1996). Neural models with cognitive implications. In Basic Processes in Reading Perception and Comprehension Models, pp.27-90.
* Chen, T,L.,& Cheng, C,H.,& Teoh, H, Jong. (2007). Fuzzy time-series basedon Fibonacci sequence for stock price forecasting. Physica A 380, 377–390.
* Chang, P, C., & Liu, C, H. (2008). A TSK type fuzzy rule based system for stock price prediction, Expert Systems with Applications 34, 135–144.
* Chang, P. C., Chen, H.L., & Jun, L.L., & Chin, Y.F,, & Celeste, S.P.Ng.(2009) . A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications,36,6889-6898.
* Chang, P. C., Wang, Y. W., & Yang, W. N. (2004). An investigation of the hybrid forecasting models for stock price variation in Taiwan. Journal of the Chinese Institute of Industrial Engineering, 21(4), 358–368.
* Chi, S. C., Chen, H. P., & Cheng, C. H. (1999). A forecasting approach for stock index future using Grey theory and neural networks. In IEEE international joint conference on neural networks (pp. 3850–3855).
* Gay, R,D.(2008). Effect of macroeconomic variable on stock market returns for four emerging economies: Brazil, Russia, India and China, International Business and Economics Research Journal 7, 8.
* Gan, C., & Lee, M., &Yong, H.H.A, & Zhang, I. (2006). Macroeconomic variables and stock market interactions: New Zealand evidence. Investment management and financial innovations,3.
* Gencay,R. & Selcuk,F. & Whitcher,B.(2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, New York .
* Goldberg,D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning , Addison-Wesley.
* George,S,A., & Kimon,P,V.(2009) .Surveying stock market forecasting techniques – Part II: soft computing methods, Expert Syst. Appl. 36, 5932–5941.
* Hadavandi,E.,& H, Shavandi , A, Ghanbari. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge based system 23, 800-808.
* Haupt, R,L., & Haupt, S,E. (1980). Practical Genetic Algorithms. Second Edition.
* Hung, J,C.(2009).A fuzzy GARCH model applied to stock market scenario using a genetic algorithm, Expert Syst. Appl. 36, 11710–11717.
* Huang, S. (2002). Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets and Systems, 132, 233-243.
* Ibrahim, M.H. & Aziz, H. (2003). Macroeconomic Variable and the Malaysian Equity market: A view through rolling su sample. Journal of Economic studies,30,22.
* Khashei,M., & M. Bijaria, G.A. Ardali.(2009). Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing 72, 956–967.
* Kimoto, T., & Asakawa, K. (1990). Stock market prediction system with modular neural network. IEEE International Joint Conference on Neural Network, 1, 1–6.
* Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, 21–24.
* Lee, J. W. (2001). Stock price prediction using reinforcement learning. IEEE International Joint Conference on Neural Networks, 1, 690–695.
* Lin, L., & Cao, L., &Wang, J.,& Zhang, Ch. (2007).The Application of Genetic Algorithms in Stock Market Data Mining Optimization, Faculty of Information Technology, University of Technology, Sydney, NSW, Australia.
* Majhi,R.,& Panda,G., & Sahoo, G.(2009). Development and performance evaluation of FLANN based model for forecasting of stock markets, Expert Syst. Appl. 36, 6800–6808.
* Mantas, C. (2006). Extraction of similarity based fuzzy rules from artificial neural networks. International Journal of Approximate Reasoning, 43, 202-221.
* Patterson, D.W.(1996). Artificial Neural Networks: Theory and Applications, Prentice Hall.
* Popoola,A. & Ahmad,K. (2006).Testing the suitability of wavelet preprocessing for TSK fuzzy models. in: Proceeding of FUZZ-IEEE: International Conference Fuzzy System Network , pp.1305–1309.
* Ramsey,J,B. (1999). The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences ,2593–2606.
* Tang, J, H., & H. F, Hsiao,& W. C, Yeh. (2010). Forecasting Stock Market Using Wavelet Transform and neural networks: An Integrated system based on artificial bee colony alghoritm, Applied soft computing , Gmodel.
* Wang, Y,F.(2002). Predicting stock price using fuzzy grey prediction system, Expert Syst. Appl. 22, 33–39.
* Yao, J., & Poh, H. L. (1995). Forecasting the KLSE index using neural networks. IEEE International Conference on Neural Networks, 2, 1012–1017.
* Yoon, Y., & Swales, J. (1991). Prediction stock price performance: A neural network approach. Proceedings of twenty-fourth annual Hawaii international conference on system science, 156–162.